An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Zheng Yin and Conall O'Sullivan and Anthony Brabazon",
  title =        "An Analysis of the Performance of Genetic Programming
                 for Realised Volatility Forecasting",
  journal =      "Journal of Artificial Intelligence and Soft Computing
  year =         "2016",
  volume =       "6",
  number =       "3",
  pages =        "155--172",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Realised
                 Volatility, High Frequency Data",
  ISSN =         "2083-2567",
  URL =          "",
  DOI =          "doi:10.1515/jaiscr-2016-0012",
  abstract =     "Traditionally, the volatility of daily returns in
                 financial markets is modelled autoregressively using a
                 time-series of lagged information. These autoregressive
                 models exploit stylised empirical properties of
                 volatility such as strong persistence, mean reversion
                 and asymmetric dependence on lagged returns. While
                 these methods can produce good forecasts, the approach
                 is in essence a theoretical as it provides no insight
                 into the nature of the causal factors and how they
                 affect volatility. Many plausible explanatory variables
                 relating market conditions and volatility have been
                 identified in various studies but despite the volume of
                 research, we lack a clear theoretical framework that
                 links these factors together. This setting of a
                 theory-weak environment suggests a useful role for
                 powerful model induction methodologies such as Genetic
                 Programming (GP). This study forecasts one-day ahead
                 realised volatility (RV) using a GP methodology that
                 incorporates information on market conditions including
                 trading volume, number of transactions, bid-ask spread,
                 average trading duration (waiting time between trades)
                 and implied volatility. The forecasting performance
                 from the evolved GP models is found to be significantly
                 better than those numbers of benchmark forecasting
                 models drawn from the finance literature, namely, the
                 heterogeneous autoregressive (HAR) model, the
                 generalized autoregressive conditional
                 heteroscedasticity (GARCH) model, and a stepwise linear
                 regression model (SR). Given the practical importance
                 of improved forecasting performance for realised
                 volatility this result is of significance for
                 practitioners in financial markets.",

Genetic Programming entries for Zheng Yin Conall O'Sullivan Anthony Brabazon